In the last few years, the rise of AI has had a major impact on the computer science curriculum for how students learn to code. As AI coding tools that can assist with tasks like generating code snippets and explaining concepts have become more prevalent, educators are being prompted to rethink their teaching approaches.
Impact of AI on ‘Academic’ Coding
The use of AI copilots in academic coding brings both opportunities and challenges. For faculty, this will require significant adjustments to curriculum and teaching methods.
Educators will need to shift emphasis away from rote learning of syntax and coding mechanics and instead prioritize higher-level concepts like software design, algorithm analysis, and testing and debugging skills that are more difficult to automate using AI copilots.
There are worries that excessive usage of AI copilots may result in a loss of logical thinking and code comprehension amongst educators, as they may prioritize quickly obtaining functional code over truly understanding the underlying programming concepts and logic.
Faculty will also hold responsibility for instructing students on ethical AI use and the potential downsides.
Additionally, the introduction of AI tools will likely require faculty to upskill and adopt new teaching methods themselves. They will need to become better at using AI copilots to generate coding examples, explanations, and assignments. Those unable to adapt may struggle to effectively incorporate the technology.
Here are some AI Tools for Coders that will make programming easy for you.
Preparing lesson plans with AI capabilities will also add to faculty workloads. Moreover, faculty may face challenges in detecting the use or overreliance on AI copilots by students in assignments and evaluating coding-related understanding.
What do Teachers think?
Via an article on IEEE, Johnny Chang, a teaching assistant at Stanford University currently pursuing a master’s degree in computer science said the following about the impact of AI copilots on learning:
“Students are early adopters and have been actively testing these tools. We should be making AI a copilot—not the autopilot—for learning.”
As there is increased use of AI, the fundamentals of coding and the skills required are evolving.
Traditionally, Introductory Computer Science courses have focused heavily on teaching code syntax and getting programs to run. However, with AI tools capable of generating code snippets, the emphasis is shifting towards higher-level concepts like testing, debugging, and problem decomposition.
Various educators are modifying their teaching strategies to accommodate this change.
Daniel Zingaro, an associate professor of computer science at the University of Toronto Mississauga, now has his students work in groups and submit videos explaining their code’s logic rather than just grading their written code. He does this to confirm whether students have written the code themselves or used AI to generate it.
“It’s an opportunity for me to assess their learning process of the whole software development [life cycle]—not just code. We need to be teaching students to be skeptical of the results and take ownership of verifying and validating them,”
The concern regarding students’ over-reliance on these AI copilot tools could hinder their critical thinking and effective learning processes.
The other challenge lies in addressing ethical considerations, such as copyright issues and bias in the training data used to create these AI models.
But there are Benefits!
Despite these challenges, various educators are optimistic about the potential benefits of AI copilots in coding education. Students will have more time to learn about complex software engineering and learn the right approaches to solve any coding problem.
Embracing AI copilots in coding education will require a collaborative effort between students and educators.
Educators should keep exploring ways in which they can integrate AI copilots into their curriculum while ensuring that students develop a solid foundation in computer science principles.
Some educators are currently using AI copilots to generate sample code snippets or explanations for complex concepts. They can then use these as starting points for classroom discussions and exercises.
Students can then analyze and evaluate these snippets and suggest necessary changes wherever needed. Using this, educators can help students develop critical thinking skills and promote a deeper understanding of coding principles.
AI copilots can also be used to provide feedback and support to students working on coding assignments or projects. They can function as a supplement to the educator who will always be there for the students’ doubts in case the copilot cannot explain it to them in a simple manner.
Conclusion
The integration of AI copilots into academics has the potential to change the way programming is taught and learned. By embracing these tools while focusing on core computer science principles and practical skills, educators can better prepare students for the rapidly developing world of software development.
